Hydrodynamic Performance Analysis of a Submersible Surface Ship and Resistance Forecasting Based on BP Neural Networks

Abstract

This paper investigated the resistance performance of a submersible surface ship (SSS) in different working cases and scales to analyze the hydrodynamic performance characteristics of an SSS at different speeds and diving depths for engineering applications. First, a hydrostatic resistance performance test of the SSS was carried out in a towing tank. Second, the scale effect of the hydrodynamic pressure coefficient and wave-making resistance was analyzed. The differences between the three-dimensional real-scale ship resistance prediction and numerical methods were explained. Finally, the advantages of genetic algorithm (GA) and neural network were combined to predict the resistance of SSS. Back propagation neural network (BPNN) and GA-BPNN were utilized to predict the SSS resistance. We also studied neural network parameter optimization, including connection weights and thresholds, using K-fold cross-validation. The results showed that when a SSS sails at low and medium speeds, the influence of various underwater cases on resistance is not obvious, while at high speeds, the resistance of water surface cases increases sharply with an increase in speed. After improving the weights and thresholds through K-fold cross-validation and GA, the prediction results of BPNN have high consistency with the actual values. The research results can provide a theoretical reference for the optimal design of the resistance of SSS in practical applications.